discretize converts a numeric vector into a factor with bins having approximately the same number of data points (based on a training set).

## Usage

discretize(x, ...)

# S3 method for default
discretize(x, ...)

# S3 method for numeric
discretize(
x,
cuts = 4,
labels = NULL,
prefix = "bin",
keep_na = TRUE,
infs = TRUE,
min_unique = 10,
...
)

# S3 method for discretize
predict(object, new_data, ...)

## Arguments

x

A numeric vector

...

Options to pass to stats::quantile() that should not include x or probs.

cuts

An integer defining how many cuts to make of the data.

labels

A character vector defining the factor levels that will be in the new factor (from smallest to largest). This should have length cuts+1 and should not include a level for missing (see keep_na below).

prefix

A single parameter value to be used as a prefix for the factor levels (e.g. bin1, bin2, ...). If the string is not a valid R name, it is coerced to one. If prefix = NULL then the factor levels will be labelled according to the output of cut().

keep_na

A logical for whether a factor level should be created to identify missing values in x. If keep_na is set to TRUE then na.rm = TRUE is used when calling stats::quantile().

infs

A logical indicating whether the smallest and largest cut point should be infinite.

min_unique

An integer defining a sample size line of dignity for the binning. If (the number of unique values)/(cuts+1) is less than min_unique, no discretization takes place.

object

An object of class discretize.

new_data

A new numeric object to be binned.

## Value

discretize returns an object of class discretize and predict.discretize returns a factor vector.

## Details

discretize estimates the cut points from x using percentiles. For example, if cuts = 3, the function estimates the quartiles of x and uses these as the cut points. If cuts = 2, the bins are defined as being above or below the median of x.

The predict method can then be used to turn numeric vectors into factor vectors.

If keep_na = TRUE, a suffix of "_missing" is used as a factor level (see the examples below).

If infs = FALSE and a new value is greater than the largest value of x, a missing value will result.

## Examples

data(biomass, package = "modeldata")

biomass_tr <- biomass[biomass$dataset == "Training", ] biomass_te <- biomass[biomass$dataset == "Testing", ]

median(biomass_tr$carbon) #> [1] 47.1 discretize(biomass_tr$carbon, cuts = 2)
#> Bins: 3 (includes missing category)
#> Breaks: -Inf, 47.1, Inf
discretize(biomass_tr$carbon, cuts = 2, infs = FALSE) #> Bins: 3 (includes missing category) #> Breaks: 14.61, 47.1, 97.18 discretize(biomass_tr$carbon, cuts = 2, infs = FALSE, keep_na = FALSE)
#> Bins: 2
#> Breaks: 14.61, 47.1, 97.18
discretize(biomass_tr$carbon, cuts = 2, prefix = "maybe a bad idea to bin") #> Warning: The prefix 'maybe a bad idea to bin' is not a valid R name. It has been changed to 'maybe.a.bad.idea.to.bin'. #> Bins: 3 (includes missing category) #> Breaks: -Inf, 47.1, Inf carbon_binned <- discretize(biomass_tr$carbon)
table(predict(carbon_binned, biomass_tr$carbon)) #> #> bin1 bin2 bin3 bin4 #> 114 115 113 114 carbon_no_infs <- discretize(biomass_tr$carbon, infs = FALSE)
predict(carbon_no_infs, c(50, 100))
#> [1] bin4 <NA>
#> Levels: bin1 bin2 bin3 bin4